System and method for assessing mobile application value

ABSTRACT

A method and system for assessing a value of a first application with respect to a use of a mobile communication device in communication with a second communication device are presented. The method entails assigning a set of proximity values that relate the use of the application to the communication between the mobile communication device and the second communication device, and determining an impact and a value based on the proximity values. The proximity values may include a relationship proximity value, a time proximity value, a geographical proximity value, an action proximity value, and one or more user-defined proximity values. The determined value may be expressed as a monetary value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/509,622, filed Jul. 27, 2009, the contents of which is incorporatedherein by reference.

FIELD OF THE INVENTION

The present invention relates to the field of computer-based andInternet/Web-based applications, as used on networks, either fixed ormobile, and mobile devices, such as cellular telephones and personaldigital assistants. More particularly, the present invention relates toa comparative value and/or a monetary value associated withcomputer-based and Internet/Web-based applications.

RELATED ART

Telecommunication system operator environments and networks are becomingmore and more open for third party products, applications, applicationdevelopment, and integration by various partners and end users. Productssuch as Service Delivery Platforms provide means for fast and agileapplication development and deployment and allow quick service andproduct introduction.

The integration and connectivity between Internet-based applications,social networks, and operator systems is constantly growing. However,the value of such new applications and integrations for the telecomoperator, in terms of aspects such as image, stickiness, position,revenues, and income, are difficult to monitor or evaluate.

Accordingly, there is a need for a system that allows an operator tomeasure and evaluate the impact of such newly introduced applicationsfor fixed networks and the impact of mobile systems and applications,whether service delivery platform (“SDP”) originated or stand-alone,with respect to metrics such as income, revenues, and operator-definedadded value. There is also a commensurate need for a system that allowsan operator to measure and evaluate the impact of existing fixed andmobile systems and applications in a similar manner.

SUMMARY OF THE INVENTION

In one aspect, the invention provides a system for assessing a value ofa first application with respect to a use of a first mobilecommunication device. The system comprises a computer server incommunication with the first mobile communication device via a networkand at least a second communication device in communication with thefirst communication device via the network. The server is configured to:detect a use of the first mobile communication device with respect tothe second communication device, wherein the detected use includes a useof the first application and a communication with the secondcommunication device; assign at least a first proximity value to thedetected use of the first mobile communication device with respect tothe detected use of the first application; use the assigned at leastfirst proximity value to determine an impact of the detected use of thefirst application upon the detected use of the first mobilecommunication device; and use the determined impact to assess a value ofthe detected use of the first application with respect to the detecteduse of the first mobile communication device.

The use of the first mobile communication device may be arevenue-generating use. The server may be further configured todetermine an amount of revenue derived from the detected use of thefirst mobile communication device, and to use the determined impact andthe determined amount of revenue to assess the value of the detected useof the first application with respect to the detected use of the firstmobile communication device. The at least first assigned proximity valuemay be selected from the group consisting of a relationship proximityvalue, a time proximity value, a geographical proximity value, anapplication proximity value, an action proximity value, and one or moreuser-defined proximity values.

The at least first assigned proximity value may include at least arelationship proximity value and a time proximity value. Therelationship proximity value may be based on a first predetermined setof criteria. The assigned time value may be based on a number ofnanoseconds, microseconds, milliseconds, seconds, or minutes that elapsebetween the detected use of the first application and a detectedresponse by the second communication device. The server may be furtherconfigured to use the assigned relationship and time proximity values todetermine an impact by computing a composite proximity distance. Thecomposite proximity distance value may be selected from the groupconsisting of a Euclidean distance, a mean distance, a Mahalanobisdistance, a Manhattan distance, a Chebyshev distance, and a Minkowskidistance. The composite proximity distance may be computed by taking asquare root of a sum of squares of the respective relationship and timeproximity values.

The server may be further configured to assign a geographical proximityvalue to the detected use of the first mobile communication device withrespect to the detected use of the first application, and to use theassigned relationship, time, and geographical proximity values todetermine an impact of the detected use of the first application uponthe detected use of the first mobile communication device. The assignedgeographical proximity value may be based on a geographical distancebetween a location associated with the detected use of the firstapplication and a location associated with the second communicationdevice. The server may be further configured to use the assignedrelationship, time, and geographical proximity values to determine animpact by taking a square root of a sum of squares of the respectiverelationship, time, and geographical proximity values. The server may befurther configured to assign a user-defined proximity value to thedetected use of the mobile communication device with respect to thedetected use of the first application, and to use the assignedrelationship, time, and user-defined proximity values to determine animpact of the detected use of the first application upon the detecteduse of the mobile communication device. The assigned user-definedproximity value may be based on a second predetermined set of criteria.The server may be further configured to use the assigned relationship,time, and user-defined proximity values to determine an impact by takinga square root of a sum of squares of the respective relationship, time,and user-defined proximity values.

The first application may be selected from the group consisting of: aninstant messaging application; a text messaging application; a socialnetworking application; an electronic mail application; a multimediamessaging application; a search application; a location application; anadvertising application; a file sharing application; a portal/intranetapplication; a CRM application; an ERP application; an address bookapplication; a database application; a process application; aprocurement application; a blog application; an internal networkcollaboration application; a video download application; an audiodownload application; a video teleconference application; an audioteleconference application; a video streaming application; an audiostreaming application; a picture album application; an Internet web siteapplication; a web browsing application; a peer-to-peer file sharing ormedia streaming application; a voice-over Internet protocol application;a payment application; a financial or investment application; aninsurance application; and a marketing application. The use of a mobilecommunication device may be selected from the group consisting ofconducting a voice telephone call, sending a text message, sending anelectronic mail message, using a web browser, uploading data,downloading data, sending information over a mobile data channel, andreceiving information over a mobile data channel.

In another aspect, the invention provides a method for assessing a valueof a first application with respect to a use of a first mobilecommunication device. The first mobile communication device is incommunication with a second communication device via a network. Themethod comprises the steps of: detecting a use of the first mobilecommunication device with respect to the second communication device,wherein the detected use includes a use of the first application and acommunication with the second communication device; assigning at least afirst proximity value to the detected use of the first mobilecommunication device with respect to the use of the first application;using the assigned at least first proximity value to determine an impactof the detected use of the first application upon the detected use ofthe first mobile communication device; and using the determined impactto assess a value of the detected use of the first application withrespect to the detected use of the first mobile communication device.

The use of the first mobile communication device may be arevenue-generating use, and the step of using the determined impact toassess a value may further comprise determining an amount of revenuederived from the detected use of the first mobile communication device,and using the determined impact and the determined amount of revenue toassess the value of the detected use of the first application withrespect to the detected use of the first mobile communication device.The at least first assigned proximity value may be selected from thegroup consisting of a relationship proximity value, a time proximityvalue, a geographical proximity value, an application proximity value,an action proximity value, and a user-defined proximity value.

The at least first assigned proximity value may include at least arelationship proximity value and a time proximity value. The assignedrelationship proximity value may be based on a first predetermined setof criteria. The assigned time value may be based on a number ofnanoseconds, microseconds, milliseconds, seconds, or minutes that elapsebetween the detected use of the first application and a detection of aresponse by the second communication device. The step of using theassigned relationship and time proximity values to determine an impactmay further comprise computing a composite proximity distance. Thecomposite proximity distance may be selected from the group consistingof a Euclidean distance, a mean distance, a Mahalanobis distance, aManhattan distance, a Chebyshev distance, and a Minkowski distance. Thecomposite proximity distance may be computed by taking a square root ofa sum of squares of the respective relationship and time proximityvalues.

The method may further comprise the step of assigning a geographicalproximity value to the detected use of the mobile communication devicewith respect to the detected use of the first application. The step ofusing the assigned relationship and time proximity values to determinean impact of the detected use of the first application upon the detecteduse of the mobile communication device may further comprise using theassigned relationship, time, and geographical proximity values todetermine an impact of the detected use of the first application uponthe detected use of the mobile communication device. The assignedgeographical proximity value may be based on a geographical distancebetween a location associated with the detected use of the firstapplication and a location associated with the second communicationdevice. The step of using the assigned relationship, time, andgeographical proximity values to determine an impact may furthercomprise taking a square root of a sum of squares of the respectiverelationship, time, and geographical proximity values. The method mayfurther comprise the step of assigning a user-defined proximity value tothe detected use of the mobile communication device with respect to thedetected use of the first application. The step of using the assignedrelationship and time proximity values to determine an impact of thedetected use of the first application upon the detected use of themobile communication device may further comprise using the assignedrelationship, time, and user-defined proximity values to determine animpact of the detected use of the first application upon the detecteduse of the mobile communication device. The assigned user-definedproximity value may be based on a second predetermined set of criteria.The step of using the assigned relationship, time, and user-definedproximity values to determine an impact may further comprise taking asquare root of a sum of squares of the respective relationship, time,and user-defined proximity values.

The first application may be selected from the group consisting of: aninstant messaging application; a text messaging application; a socialnetworking application; an electronic mail application; a multimediamessaging application; a search application; a location application; anadvertising application; a file sharing application; a portal/intranetapplication; a CRM application; an ERP application; an address bookapplication; a database application; a process application; aprocurement application; a blog application; an internal networkcollaboration application; a video download application; an audiodownload application; a video teleconference application; an audioteleconference application; a video streaming application; an audiostreaming application; a picture album application; an Internet web siteapplication; a web browsing application; a peer-to-peer file sharing ormedia streaming application; a voice-over Internet protocol application;a payment application; a financial or investment application; aninsurance application; and a marketing application. The use of a mobilecommunication device may be selected from the group consisting ofconducting a voice telephone call, sending a text message, sending anelectronic mail message, using a web browser, uploading data,downloading data, sending information over a mobile data channel, andreceiving information over a mobile data channel.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of a system for assessing a value ofan application on a mobile or fixed network according to a preferredembodiment of the invention.

FIG. 2 illustrates a block diagram of an event collection fromapplications module as used in the system of FIG. 1, according to apreferred embodiment of the invention.

FIG. 3 illustrates an exemplary set of event proximity domains as usedin the system of FIG. 1, according to a preferred embodiment of theinvention.

FIG. 4 illustrates an exemplary set of event types and correspondingproximities as used in the system of FIG. 1, according to a preferredembodiment of the invention.

FIG. 5 illustrates an exemplary set of event attributes as used in thesystem of FIG. 1, according to a preferred embodiment of the invention.

FIG. 6 illustrates a high-level set of flowcharts that implement amethod of assessing a value of a mobile application according to apreferred embodiment of the invention.

FIG. 7 illustrates a detailed flowchart for a method of assessing avalue of a mobile application according to a preferred embodiment of theinvention.

FIG. 8 illustrates an exemplary scenario for a first test case ofassessing a value of a mobile application according to a preferredembodiment of the invention.

FIG. 9 illustrates an exemplary scenario for a second test case ofassessing a value of a mobile application according to a preferredembodiment of the invention.

FIG. 10 illustrates an exemplary scenario for a third test case ofassessing a value of a mobile application according to a preferredembodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

The following is a set of definitions of relevant terms:

Operator Environment—computer network composed of the various computersystems, network equipment and software applications that compose thecore operational elements of an operator or telecommunications service(“telecom”) provider business.

Operator Application—a software or combined hardware/softwareapplication that runs inside, or connects to, a corresponding OperatorEnvironment.

Application Event—an event generated by a telecom-provider application,computer program, end user, end user device or any other entity that isconnected to the telecom operator systems. The event can be anyregistered occurrence, status change, user action or interaction orapplication action or interaction or message.

Valuable Event—an application event that is valuable to the telecomoperator. For example, a billable event is a valuable event, because itis an event that can be billed to the end user and generate revenues forthe telecom operator. Valuable events are not limited to billableevents; a valuable event can be any event the telecom operator decideshas value for the telecom operator. It is expected that most operatorswill define their Billable events as Valuable Events.

Event Proximity—a proximity measure that measures a relation between twoevents using defined criteria such as: time, user group, application,application domain, action, and any other user-defined relation ormeasure.

Event Collector—a computer system, server, or a collection of systemsand servers that is connected to a telecom operator system. Theobjective of an event collector is to collect all possible eventsgenerated by these systems. The connection can be via APIs provided bythe various systems, inline (“bump-in-the-wire”), direct connection viastandard network management interfaces (such as SNMP), and/or usingproprietary interfaces.

Event monitoring device—a device or software module that can tap or beinstalled inside a computer or computer network and analyze applicationevents, passing network packets information or otherwise otherapplicative events, and extracting meaningful event information. Anexample of such a device is a Deep Packet Inspection (DPI) device thatsits inside a computer network and extracts applicative events fromnetwork traffic. A DPI device can detect and provide information onevents such as voice calls, sending Short Message Service (SMS, alsoknown as “text” messaging), IM or other messages, billing, provisioning,and so on. Another example of an event monitoring device is a softwaremodule that runs inside a computer and analyzes the applications thatrun inside to extract meaningful events out of them. Such modules canpotentially monitor and analyze every type of event that is generated bya computer application.

Mobile network—refers to any current mobile network technology, such asGSM or CDMA, as well as evolving and new mobile technologies, such asHSPDA, W-CDMA, LTE, WiMax, and others. Mobile carriers include anycarrier that operates on any such mobile network. Notably, some mobilecarriers may allow fixed (i.e., non-mobile) personal computer webbrowsers to access their networks, and trigger the recipient to performa valuable event in accordance with a preferred embodiment of theinvention. For example, the recipient may be prompted to call the fixedsender using a mobile telephone.

Mobile device—refers to any one of a mobile or cellular telephone, apersonal digital assistant (PDA), a laptop computer having Internetaccess, messaging devices, picture-taking devices, book reading devicessuch as the Amazon Kindle, global positioning system (GPS) devices, orother specialty-type devices. It is noted that, with the exception ofthe mobile or cellular telephone, these devices may or may not havetelephone capabilities.

The system output is an indication of an application's impact onvaluable events from various specific points of views of thebeneficiary. This output can be consumed by multiple recipients and beused for multiple purposes. The following is a non-exclusive andnon-limiting list of example consumers of the system output of thepresent invention: Mobile telecom providers; fixed-line telecomproviders; other Tri/Quad-Play network providers; Internet serviceproviders; application developers; application retailers/stores;commercial entities/brands (e.g., Sony, Adidas, etc.); advertisingagencies and aggregators; third-party value creation vendors andsolutions; marketing, business intelligence and analysis entities;governmental agencies; and research and education institutions.

The following is a non-exclusive and non-limiting list of examplepotential uses of the system output of the present invention: Evaluatemobile applications for launching; establish application pricing toend-users; negotiate commercial agreement with the application provider(e.g., revenue share split); determine and design application front-enduser interface, priorities and visibility; design application marketingcampaign based on application value; determine advertising and contentembedded in the application based on its value, and pricing of same; andstudy application usage trends and patterns.

In a first exemplary usage scenario, a telecom operator measures theimpact of an instant messaging (“IM”) application on the revenue fromSMS being triggered by IM. The operator discovers that IM applicationinstalled in his network triggers a substantial amount of additional SMSevents. This measurement helps the telecom operator maximize profits bygiving free IM to its clients for the mere purpose of maximizing SMStraffic.

In a second exemplary usage scenario, a telecom operator measures theimpact of an instant messaging (“IM”) application on the revenue fromvoice telephone calls being triggered by IM. The operator discovers thatIM application installed in his network triggers a substantial amount ofadditional voice telephone call events. This measurement helps thetelecom operator maximize profits by giving free IM to its clients forthe mere purpose of maximizing voice telephone traffic.

In a third exemplary usage scenario, a telecom operator measures theimpact of an application developed by a third-party developer on therevenue from SMS or voice telephone calls being triggered by theapplication. For example, suppose that a 13-year-old girl develops aregular web page for exchanging Transformers™ icons between mobile usersand with desktop users. The application can be virally distributed amongusers. The application causes a big stream of excited phone callsbetween young users who really like it (her friends and family). Thetelecom operator measures the impact of the web page application on thenumber of phone calls between this web pages users. The telecom operator(can be either a fixed line operator or a mobile operator) can sharerevenues with the application developer; e.g., give the applicationdeveloper a fixed percentage of revenue arising from voice telephonecalls.

In a fourth exemplary scenario, the telecom operator may internally useinformation that is not monetary in nature. For example: Suppose that atelecom operator opens a mobile-based fan group on Facebook. Manyunrelated users from around the world join this group because it soundscool. The telecom operator sends an email about the group to all of itsusers that have a particular cellular telephone model. The telecomoperator uses the system to measure the impact of the Facebook fan groupon the numbers of users that joined it as a result of this specificemail. This provides valuable information to the telecom operator'smarketing department to allow better marketing of the particular celltelephone model in the future. The telecom operator could also measurenumbers of users for subset groups, e.g., the “joined young users”subgroup; the “joined users from [a particular geographical area]”subgroup; the “joined users with income >50K/year” subgroup, etc.

In a fifth exemplary scenario, the telecom operator may wish to sell theinformation to a third-party commercial entity. For example: Supposethat Adidas uses SMS to send a message with Adidas web-site link to thecell phones of a user group (say all 12-30 year old in specific area).The telecom operator configures the system to measure the impact of thisSMS on the number of entries to the web site done from the mobile devicebrowser (or desktop browser) for various user intersections, includingthe following: Granular age groups (12-16, 17-20, 21-25, 26-30);geographical locations (East London, South London, West London); mobiledevice types (PDAs, IPhone, Nokia 63xx, etc.); a combination of theabove; and/or any other collectible event info that is collected and caninterest Adidas. The telecom operator sells this information to Adidasto allow better and more accurate ad targeting and user profiling in thefuture. It is noted that in this particular case, the valuable event isnot the SMS itself, but rather the web site entries.

Referring to FIG. 1, an application impact measurement system 500according to a preferred embodiment of the present invention is shown.The telecom operator environment 100 contains a multitude of systems,servers, applications and entities. The event collector subsystem 200connects to various systems in the telecom operator environment andpulls, scans, or otherwise receives and collects all possible generatedevents. The event collector 200 connects to the telecom operator systemsusing the various interfaces they provide, such as socket, ftp, logfacilities, SNMP, web services, etc. For systems with no suchinterfaces, the collector 200 can intercept some or all of the events bytapping into the network and analyzing network packets, either directlyor using an event monitoring device, such as a DPI device. An optionalevent monitoring device 101 may be used to monitor the telecom operatornetwork. This type of monitoring can be achieved by standard means ofduplicating and mirroring telecom provider network traffic to the eventmonitoring device 101. The device 101 can then report to the eventcollector 200 on various applicative events it detects and that aregenerated by the various systems inside the telecom operator environment100. An event storage/repository subsystem 300 can be a standarddatabase or any other system that can store large quantities of eventinformation items.

The event correlation system 400 includes a configuration module 402 anda computation system 401. The event correlation system 400 is configuredby the user regarding the application(s) for which the impact is to becomputed. The event correlation computation system 401 is responsiblefor scanning the events stored in the event storage module 300 accordingto the configuration stored in the event correlation configurationsystem 402, and computing application impact for requested applications.

Referring to FIG. 2, a block diagram illustrates the collection ofevents generated by different applications that run in the telecomoperator environment. These events can be collected using many possiblewell known methods and interfaces.

Referring to FIG. 3 and Table 1 below, various exemplary event proximitydomains are demonstrated and described:

TABLE 1 Event proximity domains Proximity Domain Description ExamplesTime proximity Time proximity (timeproxim) is the Time in seconds,milliseconds, etc., time proximity between EVENT and between theoccurrence of events VALUABLE_EVENT. It can be the time in secondsbetween the occurrence of the events or any other numerical time relatedindication. Relation This is basically social proximity. The value canbe small for users that are 1st proximity Small figure can indicateclose friends, degree friends, bigger for users that are 2nd family,etc., where bigger numbers can degree friends, and so on. indicatefriends-of-friends (2nd, 3rd, For example, consider user X thatgenerates Nth degree friends, and so on). For an event which directlygenerated valuable example - it can be the social network events in anouter proximity of relations - X friendship relationship between thesends an IM to his friends which forward to users that are associatedwith EVENT their friends which make a voice call. So and VALUABLE_EVENT.Family the initial IM and the voice call events have relationship beingsaved in the telecom two hops of relation between them. As a providesCRM system, friends in an result - the relational proximity is not asaddress book saved on the telco small as in between direct friends butstill provider network, etc. relevant for the voice call. Anotherexample is an Indirect valuable event from family/college in same circleof recipients. So a kid sends an IM to one parent, and this causes theOTHER parent does to initiate a phone call - a billable event back tothe kid. The initial IM and the phone call have small relation proximitybecause they are events inside the family (as identified by telecomprovider records or configuration). Application Application Proximity isthe Example for application with small proximity application proximitybetween the proximity can be voice call and SMS, two applications thatgenerated with Facebook applications, SMS and IM and so EVENT andVALUABLE_EVENT. on. Example: A user uploads a picture to Applicationproximity can be a user Facebook, which is notified to the user'sdefined numeric value. For example - spouse, who then downloads thepicture to two applications that serve the same her mobile device andthen uploads it from function or exist under the same the mobile deviceto a Picasa application context can have application proximity and/or toa blog. There is a measurable of 1. Applications that are separate, butapplication domain proximity between the occasionally communicate withone Facebook, Picasa, and blog applications. another by passinginformation or messages have an application proximity of 2. Whilecompletely unrelated applications will have a big application proximityvalue and so on. Application proximity can be configured by the userbecause new applications or new uses to existing applications can makeunrelated applications ‘closer’ or basically put two existingapplications under the same context. Action Action proximity refers torelated Example: A first user uses a first proximity actions that may beperformed in the application to upload a picture of a bicycle. contextof same or different A second user, not related to or known byapplications, such as, for example, the first user, uses an unrelatedsecond uploading and downloading a picture. application, such as aportal search engine, to download the picture, because the second userlikes the picture. Although the users and the applications are notclosely related, the actions are related, thereby yielding an actionproximity. User-defined This is a set of user-defined, For example auser can decide that any event proximity configurable, proximity valuesbetween events between family members have small EVENT andVALUABLE_EVENT. proximity value while events between User-defined eventproximity is a unrelated users have bigger proximity value. configurablenumerical value that the Another, slightly more complex example for usercan define as a proximity indicator a configured user-defined eventproximity is between any two events. “any events from social networkingapplications with user age <25 and time proximity of less than 5″ willhave user- defined event_proximity[1] of 2, etc. Another example is acampaign - user- defined proximity can also refer to a viral event suchas email to multiple friends that is being forwarded between people,group or event invite and so on. So for example - the system may beconfigured or even dynamically set two events to have a smalluser-defined-proximity[x] because they relate to two users that belongto a specific ad campaign, email message forward, or group invite ormessage. Geographic or This is the geographical proximity For example -the geographical proximity Location between two users that areassociated of two events from users that reside in the proximity withEVENT and same geographical area (mobile network VALUABLE_EVENT. It canbe a cell, building block, etc.) is smaller than the user-defined valuethat describes their geographical proximity of these events proximity inmeters or any other GPS when the users are far from each other (on orphysical distance information. The different parts of the globe).Another proximity can also refer to other event example is two Instantmessage or SMS attributes other than user, so that two events thatdiscuss a specific single location IM message events that discuss the(pub) or two locations (gym, gym-pool) that same location (a certainpub) can be are geographically close to each other can closegeographical proximity. have a small proximity figure while similarevents that talk about two completely different locations will have abigger geographical proximity value.

In a preferred embodiment of the invention, all proximity measuresshould be normalized according to some scale so that ultimate valuesare, for example, between 0 and 1. Additional proximity measures can bedefined. Additionally, the user-defined allows for extensibility to anycriteria, as required. Additionally, each proximity measure canpotentially be an array or a set of numerical values. For example, timeproximity between two SMS messages can be an array or set that includesmultiple numerical values: 1) The time delta between the initiation oftwo messages; 2) the time delta between the receive times for the twoevents by interim systems; 3) the time delta between the actual users'receipts of the two events; etc.

Referring to FIG. 4, a diagram illustrates an exemplary set of eventtypes and corresponding event proximities for specific applications Aand B. The diagram illustrates how two events, events X and Y, generatedby application A and B may be related to a specific valuable event V.For example, valuable event V could be an initiation or completion of avoice call, which is usually valuable to a telecom operator. The planeangle represents the domain and linear distance from the valuable eventrepresents the proximity value. As one can see in this diagram, eachevent X and Y has different proximity values for the various domains(time, user_defined_1, friendship). Event Y is closer to the valuableevent V than event X in all three domains, and therefore, event Y isdeemed to be “closer” to event V than event X. If events X and Y werethe only two events ever generated by applications A and B, then onecould clearly say that application B is closer to event V thanapplication A. As a result, the impact of application B on event V isdetermined to be larger than that of application A on event V (i.e., itis more likely that V is a result of B than that it is the result of A).

Referring to FIG. 5, the attributes that the system keeps for each eventthat it saves are shown. The various fields and description are below:

Unique ID—a global unique identifier that distinguishes this event fromall other events.

Time—a time field that saves the time on which this event was generatedor captured.

Source—the end user, or system, that is associated with the initiationof this event.

Destination—the end user, or system, that is associated with thedestination of this event. In particular, it is noted that in someinstances, there are destinations to the actions performed by a userusing an application via a mobile device to other recipients.

Application—the application that generated this event.

Campaign—The advertising or marketing or spontaneous viral campaign towhich this event belongs. This field can be used to create relationshipsbetween event generated by campaigns and valuable events. This fieldcould possibly be set by a user as part of the system configuration.This field can also be dynamically set by the system for various eventsthat are determined by the system to be related to the same campaign,process flow, or any other logic.

Type—event type (SMS, MMS, email, instant message, presence or statuschange, any other transaction confirmation or failure, etc.).

Billing—notes to what capacity the event is billable. This field couldpotentially include several levels of billing.

Value—notes the value of this event. This field can be a numberrepresenting U.S. dollars or any other monetary value or otherrepresentation of value.

Location—the geographical location associated with the event. It can beend-user location, a Global Positioning System (GPS) information passedin a message, etc. The location can also be a location that is extractedfrom the content of the event or message itself.

User-Defined attributes (0 . . . N)—user-defined key-value pairs thatcan be configured to accompany the saved occurrence of a specific event(by application and type for example). These attributes may or may notaccompany the event when it is captured/recorded and could potentiallybe added at a later time by the end user that configures the system.

Referring to FIG. 6, three flowcharts illustrate a general process for amethod of assessing the value of mobile applications according to apreferred embodiment of the invention. In the event collection process,the event collector collects applicative events from the variousapplications in the telecom provider network. This collection can becontinuous or on-demand. This can be achieved in many possible waysknown in the industry. Some, but not all, include the following:

-   -   Direct connection/interface to various applications in the        telecom provider network by means of inter-process        communication, remote procedure calls, sockets, various        distributed computing interfaces (Corba, DCOM, SOAP, HTTP, RPC,        etc.).    -   Indirect connection/interface to various applications in the        telecom provider network by means of analyzing offline        transaction logs or other application generated files that        contain applicative events.    -   Receiving events or information from a third-party entity or        service providers and using it in the event collection process.        For example, a telecom operator may receive a list of public        emails of celebrities from a gossip news agency and these help        defining events with these emails as “valuable” during the event        collection process.    -   Directly analyzing applications that run in the computing        environment by means of software modules that track applications        on each computer.    -   Connection to a event monitoring or DPI device that is analyzing        network traffic on the telecom provider network environment. The        events are collected from the DPI device either directly or via        mediation system (e.g., database based, or other).

It is noted that the event collection phase may include a dynamicconfiguration based on learning or feedback from previous iterations oruser templates.

In the impact computation process, a computation application impact isperformed upon user request and after the various parameters have beenconfigured into the configuration system 402. The computation algorithmis as follows:

-   -   1. User decides to compute the impact of application A on all        valuable events (or a subset of them as configured into        configuration system 402).    -   2. User configures system 402 with the relevant application (A),        the list of valuable events the user wants to measure (for        example—only measure the impact of A on voice calls, SMSs,        etc.), and other desired parameters.    -   3. User configures system 402 with various proximity        relationships between events.    -   4. User selects a specific application impact algorithm out of a        set of algorithms that is programmed into event correlation        computation system 401.    -   5. User initiates a computation request to the system 401.    -   6. The system runs a correlation algorithm that scans the event        storage system and computes the impact of application A on the        configured valuable events.    -   7. The system returns a value that represents the impact of        application A on the configured valuable events. In the examples        below we demonstrate two possible outputs:        -   The value can be a relative number that is used for            comparison purposes with other similar results to determine            which applications have a greater impact and which have a            lesser impact.        -   The value can be the summary of the real value of all            valuable events caused by application A (so the value will            be in $ or any other currency).

In the configuration process, a user or another system or the systemitself is able to configure the system to provide the required output.Dynamic configuration includes the following actions:

-   -   Configuration can be triggered and done while events are being        collected in the event collection phase, according to the        ongoing events analysis and system deductions from the event        collection.    -   Specific meta-data and event attributes can be added and set        while events are being collected. For example, event        “user-defined” attributes can be set to various values that        represent system insights about the events as they are being        collected.    -   Various configuration states can co-exist and be turned “on” and        “off” while the system collects the events. So, for example, if        the system identifies a specific event flow that lasts for three        hours, say SMS messages flowing between soccer fans during a        soccer game, the system can tag the relevant events with this        information as “SMS message by soccer game fan” for the time of        the game, and then stop doing that once the game is over.

Static configuration happens before or after the event collection phase.The static or dynamic configuration includes the following items:

-   -   1. The application for which the impact shall be measured. For        example, “Social network connectivity service/application”,        “email application”, or “IM application”.    -   2. Cause and effect—the system can be configured, for any        possible event couples/streams/sets, to determine whether an        application event is the cause of the revenue-generating event,        or not (e.g., the billable event was bound to happen, and the        application event was a mere coordination method/step), in which        case no revenue will be credited to the application event, and        it will be dropped from the computation.    -   3. Known event flows The system can be configured to identify,        for various event couples, sets, or flows, a “reverse process”,        in which a billable event is known, and a triggering event that        will occur prior to the billable event is generated to create        and/or manage the billable event. The system can be configured        to credit the valuable/billable event to the triggering event or        not to credit it. In this context, “crediting” means that the        triggering event will take part in the computation, and        proximity values between these events may be modified by the        system.    -   4. Event attributes—any event attribute can be set and possibly        updated or configured while the system is collecting the events.        -   Specifically user-defined event attributes for specific            events or groups of events. This could also be according to            online analysis of events as the system collects them. As an            example, two or more email message events with the same            subject (e.g., “party invitation”) could trigger a state in            which user-defined-attributes for events from these users            for a specific time span (e.g., a day before the party) are            set to reflect the fact that the users are under the same            “campaign”. Therefore, these users will be marked as “under            party campaign” for a fixed amount of time, and all events            from/to these users could have one of its            user-defined-proximity fields set to “party campaign”, etc.    -   5. Proximity values (user-defined and other) between events        according to criteria based on the event attributes. A        determination of which algorithm to use for event value        computation may also be performed. For example:        -   A person sends an IM to one call center representative, and            another representative calls back. These two representatives            belong to the same corporation and all events are under the            same cooperation flow. Assuming the system is configured to            identify these facts, the system can reflect these facts in            the attributes for the events collected so that the            telephone call event is configured to have closer            relation-proximity (or other proximity) with the original IM            even though the telephone call arrives from a non-related            user.        -   There is a certain known flow that causes a valuable event            to happen. Notably, the events do not necessarily have time            proximity. For example, an event could be an eBay auction            that can take months. If the system is configured to know            this fact, the system can choose to dynamically configure            the events of this flow, as the events are collected, to            have specific attributes and proximity measures for this            specific flow.    -   6. Value figure for various telecom operator events defined as        “valuable” (e.g., telephone call, SMS, etc.). Value can be        monetary or other. An event can have any number of value        attributes; therefore, the event may have different values with        respect to different interested parties.    -   7. Output type—The output type could be either an        “application-value”, which is a numerical value that has no        direct monetary value but rather represents how a specific        application performs compared to other application, or the        output type could be a number that represents real world value.    -   8. Algorithm method—the system can be statically or dynamically        be configured to choose a specific value computation algorithm        for any specified set of events.

Referring to FIG. 7, a possible application impact computation algorithmis described below using the following diagrams and equations. Note thatthe system 401 should be able to use any other machine computablealgorithm, as programmed into it. In this specific algorithm example,the proximity value is required to be an integer number greater than orequal to one. A value of one denotes “close” proximity between events;the larger the number, the more remote the events are with respect toone another.

-   -   1. User initiates a computation request to the system.    -   2. For every Event(n) associated with application A, the        algorithm scans all the valuable events in a pre-configured        proximity radius.        It is noted that the number of valuable events included in the        computation is limited by not including events that are too        distant, or not relevant according to some other criteria, as        these are not relevant for the impact and may overload the        computation. The relevant maximum proximity value should be        configured in system 402.    -   The algorithm computes the likelihood between Event(n) and        VEvent(m) and then multiplies it by the value of VEvent(m). Once        it does that for all valuable events that are close enough to        Event(n), it moves to Event(n+1) and so on.    -   3. The algorithm makes sure not to scan/count the same valuable        event twice (so that application that is event ‘heavy’ but cause        only little valuable events is not biased favorably). This can        be done by marking valuable events that were “used” already and        not going though them again, or by taking only the single        nearest likelihood value between some application A event and        each valuable event.    -   4. For the sake of this algorithm, it is assumed that Event and        VEvent from the same user can be taken into account only if        specifically configured into the system by the user, otherwise        they will be discarded.    -   5. The algorithm sums all these multiplications to produce a        compound “application-value” number. Note that in this specific        implementation, this number is relative and will not have        monetary value.    -   6. Alternative impact computation: In order to obtain        application monetary added value, if required by the        configuration, a slightly simpler algorithm could summarize the        configured monetary value of all occurrences of valuable events        that were a result of events from A within a compound, or even        specific, proximity value. This number will give the amount of        money that was generated as a result of events coming from        application A triggering billable events (see “alternative        impact calculation” in the equation below).

In a preferred embodiment of the invention, the compound proximity(distance) between event and valuable event is the root of the sumsquare distances between these events (Euclidean distance), as providedby Equation 1:

                       Equation  1-Distance  Between  Events$\begin{matrix}{{{DISTANCEOF}\left( {{EVENT},{VALUEABLE\_ EVENT}} \right)} =} \\\sqrt{\begin{matrix}\begin{matrix}{{e_{1} \cdot {timeproxim}^{2}} + {e_{2} \cdot {relationproxim}^{2}} +} \\{{e_{3} \cdot {appproxim}^{2}} +}\end{matrix} \\\begin{matrix}{{e_{4} \cdot {geoproxim}^{2}} + {e_{5} \cdot {actionproxim}_{1}^{2}} +} \\{{e_{6} \cdot {userdefineproxim}_{1}^{2}} + \ldots}\end{matrix}\end{matrix}} \\\left( {e_{\lbrack n\rbrack} = {0/1}} \right)\end{matrix}$

A generic representation of compound distance equation between event andvaluable event, taking all proximity methods into account and wherevariables e(n) are used to toggle (i.e., disable/enable) specificproximity measures (allowing multiple formulas according to systemconfiguration).

In a preferred embodiment of the invention, Equation 2 yields thelikelihood of VALUABLE_EVENT happening as a result of EVENT is thereciprocal of the distance between these two events (note: this is not aprobabilistic likelihood).

                      Equation  2-Likelihood  between  Events$\begin{matrix}{{{LIKELIHOOD}\left( {{VALUEABLE\_ EVENT},{EVENT}} \right)} =} \\\frac{1}{{DISTANCEOF}\left( {{EVENT},{VALUABLE\_ EVENT}} \right.}\end{matrix}$

In a preferred embodiment of the invention, an impact value may becomputed using Equation 3 by summing the value of valuable eventsmultiplied by the likelihoods of each valuable event happening in as aresult of events from application A, showing the impact of all eventsfrom application A on valuable events (VEvent is valuable event), as anumeric value.

                          Equation  2-Impact  Computation$\begin{matrix}{{{impact}\left( {{{APP}(A)},{VALUEABLE\_ EVENTS}} \right)} =} \\{\sum\limits_{\underset{Events}{n:{AppA}}}{\sum\limits_{\underset{\underset{{distance} < {\max \; \_ \; {distance}}}{Events}}{m:{Valuable}}}{{{LIKELIHOOD}\begin{pmatrix}{{{EVENT}(n)},} \\{{VEVENT}(m)}\end{pmatrix}} \cdot {{EVENTVALUE}(m)}}}}\end{matrix}$

Equation 4 provides a variant formula computes a monetary rather thanrelative value (e.g., based on currency as taken from the valueattribute of the relevant valuable events).

                  Equation  4-Alternative  Impact  Computation$\begin{matrix}{{{impact}^{*}\left( {{{APP}(A)},{VALUEABLE\_ EVENTS}} \right)} =} \\{\sum\limits_{\underset{Events}{n:{AppA}}}{\sum\limits_{\underset{\underset{{distanceof} < {\max \; \_ \; {distance}}}{Events}}{m:{Valueable}}}{{EVENTVALUE}(m)}}}\end{matrix}$

The following formulae are derived from the general formulas in theprevious section, and are used for the test cases that follow. Equation5 provides a possible event distance computation between event andvaluable event taking only relation proximity into account. Accordingly,in this example, event proximity between two events is computed onlyfrom family or friendship relation and not from the time delta betweenthe two events:

DISTANCEOF(EVENT,VALUEABLE_EVENT)=√{square root over(relationproxim²)}  Equation 5—Relation Only Proximity

Equation 6 provides a possible event distance computation between eventand valuable event taking time proximity and relation proximity intoaccount:

DISTANCEOF(EVENT,VALUEABLE_EVENT)=√{square root over(timeproxim²+relationproxim²)}  Equation 6—Time and Relation Proximity

Equation 7 provides a possible event distance computation between eventand valuable event taking event time, and user defined proximity intoaccount:

DISTANCEOF(EVENT,VALUEABLE_EVENT)=

√{square root over (timeproxim²+userdefinedproxim₁ ²)}  Equation 7—Timeand User Defined Proximity

Equation 8 provides a possible event distance computation between eventand valuable event taking event time, event application proximity, andevent user defined proximity into account:

         Equation  8-Time, Application  and  User  Defined  Proximity$\begin{matrix}{{{DISTANCEOF}\left( {{EVENT},{VALUEABLE\_ EVENT}} \right)} =} \\\sqrt{{timeproxim}^{2} + {appproxim}^{2} + {userdefinedproxim}_{1}^{2}}\end{matrix}$

Equation 9 provides a possible event distance computation between eventand valuable event taking event time, event geographical position, andevent user defined proximity into account.

        Equation  9-Time, Geographical  and  User  Defined  Proximity$\begin{matrix}{{{DISTANCEOF}\left( {{EVENT},{VALUEABLE\_ EVENT}} \right)} =} \\\sqrt{{timeproxim}^{2} + {geoproxim}^{2} + {userdefinedproxim}_{1}^{2}}\end{matrix}$

Equation 10 provides a possible event distance computation between eventand valuable event taking time proximity, application proximity, andrelationship proximity into account:

         Equation  10-Time, Application  and  User  Defined  Proximity$\begin{matrix}{{{DISTANCEOF}\left( {{EVENT},{VALUEABLE\_ EVENT}} \right)} =} \\\sqrt{{timeproxim}^{2} + {appproxim}^{2} + {relationproxim}^{2}}\end{matrix}$

It is noted that the distances computed in Equations 5-10 above arenonlimiting examples of Euclidean distances, i.e., each distance isexpressed as a square root of a sum of squares of individual proximityvalues. However, distance may be computed using other formulae andmethods. For example, distance may be expressed as a mean, by simplysumming the normalized individual proximity values and then dividingthat sum by the number of individual proximity values. A second exampleis Mahalanobis distance, which normalizes the proximity values based ona covariance matrix, thereby rendering the distance metricscale-invariant. A third example is Manhattan distance, which measuresdistance following only axis-aligned directions. A fourth example isChebyshev distance, which measures distance using an assumption thatonly the most significant dimension is relevant. A fifth example isMinkowski distance, which is a generalization that takes each of theEuclidean distance, the Manhattan distance, and the Chebyshev distanceinto account.

Referring to FIG. 8, a first exemplary test case involves an instantmessage (IM) triggering a voice call between family members. This usecase has three flavors.

Test Case 1A—First Flavor

-   -   1. User K sends an instant message via instant messaging        application A to his mother, user M.    -   2. After 2 minutes, user M makes a voice call to user F (user        K's father) and talks for 10 minutes (this is a valuable event        which has revenue associated with it).    -   3. The system is instructed to compute the application value of        the “IM application”.

Test Case 1B—Second Flavor

This flavor is similar to the first flavor, except that the voice callis shorter and yields just 0.5 USD$. Accordingly, the computed impact ofthe IM application should be smaller according to the originalalgorithm, or the same according to the alternative impact computation.

Test Case 1C—Third Flavor

Again, this flavor is similar to the first flavor, except that the voicecall is triggered after 1 hour (60 minutes) instead of 2 minutes.

TABLE 2 Test Case #1 Events Use case 1 events Use case 1 events Use case1 events (1^(st) flavor) (2nd flavor) (3rd flavor) IM EVENT-1: ‘IM sendEVENT-1: ‘IM send EVENT-1: ‘IM send application message’ event message’event message’ event A events from user K to user M from user K to userM from user K to user M Unique ID 123456 Unique ID 124536 Unique ID17896 Time: 12:38 Time: 12:38 Time: 12:38 Source: User K Source: User KSource: User K Application: IM Application: IM Application: IM Campaign:none Campaign: none Campaign: none Type: instant message Type: instantmessage Type: instant message Billing: none Billing: none Billing: noneValue ($ or other): Value ($ or other): Value ($ or other): none nonenone Location: SW5 9PR Location: SW5 9PR Location: SW5 9PR UserDefinedUserDefined UserDefined attributes(0 . . . N): none attributes(0 . . .N): none attributes(0 . . . N): none Valuable EVENT-2: ‘Phone EVENT-2:‘Phone EVENT-2: ‘Phone events in call’ event from user call’ event fromuser call’ event from user the M to user F M to user F M to user Ftelecom Unique ID 444232 Unique ID 555232 Unique ID 666232 operatorTime: 12:40 Time: 12:40 Time: 13:38 network Source: User M Source: UserM Source: User M Application: Voice Application: Voice Application:Voice Campaign: none Campaign: none Campaign: none Type: phone callType: phone call Type: phone call Billing: yes Billing: yes Billing: yesValue ($ or other): Value ($ or other): 5 Value ($ or other): 10 minuteslandline minutes landline call 10 minutes landline call time (assume 1time (assume 1 USD) call time (assume 1 USD) Location: SW5 9PR USD)Location: SW5 9PR UserDefined Location: SW5 9PR UserDefined attributes(0. . . N): none UserDefined attributes(0 . . . N): none attributes(0 . .. N): none

In Test case #1, the user configures the system to only count eventsthat are up to a distance of 3, and the user configures the system touse the distance equation shown in Equation 6 (i.e., one out of manypossible distance equations):

DISTANCEOF(EVENT,VALUEABLE_EVENT)=√{square root over(timeproxim²+relationproxim²)}  Equation 6—Test Case #1 Formula

TABLE 3 Test Case #1 Impact Calculation Time proximity (EVENT-1,EVENT-2) DISTANCE OF Algorithm output (minutes, but can be Relation(EVENT-1, EVENT-2) Impact = normalized toany unit to proximity(according to Likelihood = Likelihood(event-1, Algorithm output alignwith the other (EVENT-1, equation from 1/DISTANCE event-2) * valueAlternative impact distance elements) EVENT-2) FIG. 8) OF (event-2) (seeparagraph [0064]) 1st flavor 2 1 (events from 2.23 (so under 3 0.4470.447 1 USD same family and taken under members) account) 2nd flavor 2 12.23 0.447 0.223 1 USD 3rd flavor 60 1 60.008 0.016664 0.016 0 USD

Referring to Table 3, an exemplary impact calculation for Test case #1is shown. It is noted that the impact of the IM application is bigger inthe Test case 1A because the voice call is longer. It is further notedthat in Test case 1C, the impact is much smaller, and the alternativeimpact is zero, because the time proximity between the voice call andthe IM is too remote In other words, the IM application is determined tohave had no real impact on the voice call.

Referring to FIG. 9, a second exemplary test case involves a socialnetwork picture upload triggering an SMS transmission between friends.

Test Case 2

-   -   1. User K (Kevin) uploads a picture to his Facebook site.    -   2. After three minutes, user D (Dana) looks at her Facebook        account and sees that her friend Kevin uploaded a new picture.    -   3. User D writes a comment under the picture.    -   4. The comment triggers Facebook to send an SMS to user K saying        “Dana has commented about your photo”.

TABLE 4 Test Case #2 Events Facebook EVENT-1: ‘upload EVENT-2: ‘friendEVENT-3: ‘write application picture’ event from picture upload comment’event from events user K to his profile notification’ event user D toFacebook on Facebook from Facebook to Unique ID 663456 Unique ID 6734346user D Time: 12:58 Time: 12:38 Unique ID 663456 Source: User D Source:User K Time: 12:39 Target: User K (via Target: User K (via Source: UserD (via Facebook) Facebook) Facebook) Application: Facebook Application:Target: User D client Facebook client Application: Campaign: noneCampaign: none Facebook client Type: picture comment Type: socialnetworks Campaign: none Billing: none picture upload Type: SocialNetworks Value ($ or other): none Billing: none picture upload Location:SW5 9PR Value ($ or other): Billing: none UserDefined none Value ($ orother): attributes(0 . . . N): none Location: SW5 9PR none UserDefinedLocation: SW5 9PR attributes(0 . . . N): none UserDefined attributes(0 .. . N): none Valuable EVENT-4: ‘SMS’ events in event from the Facebookto user K telecom Unique ID 556232 operator Time: 13:00 network Source:Facebook Target: User K Application: SMS (sent by Facebook) Campaign:none Type: phone call Billing: yes Value ($ or other): 2 USD (assumedcost of SMS) Location: SW5 9PR UserDefined attributes(0 . . . N): none

In Test case #2, the user configures the system so that “Facebook” andSMS events have an application proximity equal to 1 (i.e., they aredeemed to be “near” applications). Additionally, the user configures thesystem to only count events that are up to a distance of 3, and the userconfigures the system to use the distance equation shown in Equation 10(i.e., one out of many possible distance equations):

$\begin{matrix}{\begin{matrix}{{{DISTANCEOF}\left( {{EVENT},{VALUEABLE\_ EVENT}} \right)} =} \\\sqrt{{timeproxim}^{2} + {appproxim}^{2} + {relationproxim}^{2}}\end{matrix}} & {\; {{Equation}\mspace{14mu} 10\text{-}{Test}\mspace{14mu} {case}\mspace{14mu} {\# 2}\mspace{14mu} {Formula}}\;}\end{matrix}$

TABLE 5 Test Case #2 Impact Calculation Algorithm output Time RelationApplication DISTANCE Likelihood = Algorithm Alternative impact proximityproximity proximity OF 1/DISTANCEOF output (see paragraph [0064])Event-1 22 1 1 22.04 0.045 Event-4 Event-2 21 1 1 21.04 0.047 Event-4Event-3 2 1 1 2.04 0.49 0.49 * 2 = 0.98 2 USD Event-4

Referring to Table 5, an exemplary impact calculation for Test case #2is shown. Three different likelihood figures are generated by theformula, and the algorithm chooses the third one because it has thelargest “likelihood” figure. Therefore, the impact of the Facebookapplication on valuable events (a single SMS in this example) is 0.98 or2 USD, depending on the computation method chosen.

Referring to FIG. 10, a third exemplary test case involves a viral partyemail triggering multiple voice telephone calls.

Test Case 3

-   -   1. User K (Kevin) initiates a viral party invitation to a group        of friends, asking them to forward the invitation and also        asking everyone that's coming to the party to call him for        confirmation.    -   2. User G (Gary) receives the email and calls User K (Kevin) to        confirm.    -   3. User D (Dana) receives the email and forwards to her friend,        user M (Michelle), then calls Kevin to confirm.    -   4. User M (Michelle) receives the email and calls Kevin to        confirm.

Referring to Table 6, as the system collects these events, it identifiesthey all belong to the same email, for example, by using the emailtitle. The system itself, while collecting these events, marks the usersas being part of the “email campaign” for X hours, as they all receiveor send email with the same title. As a result, the events that theseusers generate are marked with a user-defined attribute that specifiesthis. The user-defined attribute could include campaign name, ID, etc.The system also detects that the users, while under the campaignlifetime, have made voice telephone calls. This causes the system toconfigure the email application events and the voice events to have a“close” application proximity value (e.g., 1).

TABLE 6 Test Case #3 Events Email EVENT-1: ‘party EVENT-2: ‘partyEVENT-3: ‘party invite application invite email’ event invite email’event email’ event from user events from user K to user G from user K touser D D to user M Unique ID 6734346 Unique ID 663456 Unique ID 663456Time: 12:38 Time: 12:42 Time: 12:50 Source: User K Source: User KSource: User D Target: User G Target: User D Target: User M Application:email Application: email Application: email Campaign: none Campaign:none Campaign: none Type: regular email Type: regular email Type:regular email Value ($ or other): none Billing: none Billing: noneLocation: SW5 9PR Value ($ or other): Value ($ or other): noneUserDefined none Location: SW5 9PR attributes(0): ‘party Location: SW59PR UserDefined campaign with users UserDefined attributes(0): ‘party K,D, G, M’ attributes(0): ‘party campaign with users UserDefined campaignwith K, D, G, M’ attributes(1 . . . N): none users K, D, G, M’UserDefined UserDefined attributes(1 . . . N): none attributes(1 . . .N): none Valuable EVENT-4: ‘’voice call’ EVENT-5: ‘’voice EVENT-6:‘’voice call’ events in event from User D to call’ event from event fromuser G to the user K user M to user K user K telecom Unique ID 556232Unique ID 556622 Unique ID 556712 operator Time: 13:00 Time: 13:15 Time:13:25 network Source: User D Source: User M Source: User G (voice callsTarget User K Target: User K Target: User K in this use Application:voice Application: voice Application: voice case) Campaign: noneCampaign: none Campaign: none Type: phone call Type: phone call Type:phone call Billing: yes Billing: yes Billing: yes Value ($ or other):2.5 Value ($ or other): Value ($ or other): 5 USD 3 USD USD Location:SW12 9PR Location: SW5 4PR Location: SW5 12PR UserDefined UserDefinedUserDefined attributes(0): ‘party attributes(0): ‘party attributes(0):‘party campaign with users campaign with campaign with users K, D, G, M’users K, D, G, M’ K, D, G, M’ UserDefined UserDefined UserDefinedattributes(1 . . . N): none attributes(1 . . . N): none attributes(1 . .. N): none

In Test case #3, the user configures the system to compute theapplication value of the “email application” by using the distanceequation shown in Equation 8 (i.e., one out of many possible distanceequations):

                         Equation  8-Test  case  #3  Formula$\begin{matrix}{{{DISTANCEOF}\left( {{EVENT},{VALUEABLE\_ EVENT}} \right)} =} \\\sqrt{{timeproxim}^{2} + {appproxim}^{2} + {userdefineproxim}_{1}^{2}}\end{matrix}$

TABLE 7 Test Case #3 Impact Calculation Algorithm partial outputAlternative Time Relation Application DISTANCE Likelihood = 1/ Selectedfor Algorithm partial impact proximity proximity proximity OF DISTANCEOF computation output (see paragraph [0064]) Event-1 22 1 1 22.04 0.045NO (not minimal Event-4 distance) Event-1 37 1 1 37.04 0.027 NO (notminimal Event-5 distance) Event-1 47 2 (friend of 1 47.05 0.021 NO (notminimal Event-6 friend) distance) Event-2 18 1 1 18.05 0.055 YES(closest to 0.055*2.5 = 0.135  2.5 Event-4 valuable event 4) Event-2 332 1 33.07 0.030 NO (not minimal Event-5 distance) Event-2 43 1 1 43.020.023 NO (not minimal Event-6 distance) Event-3 10 1 (two 1 N/A (same NANO (same user) Event-4 events from user) same user) Event-3 25 1 1 25.030.04  YES (closest to 0.04*3 = 0.12 3 Event-5 valuable event 5) Event-335 2 1 35.07 0.028 YES (closest to 0.028*5 = 0.14  5 Event-6 valuableevent 6) Final 0.135 + 0.12 + 2.5 + 3 + 5 = result: 0.14 = 0.395 10.5USD

Referring to Table 7, an exemplary impact calculation for Test case #3is shown. The accumulative contribution of the email application, inthis example, to the valuable events (i.e. voice calls), is determinedto be 0.395, or, using the alternative algorithm, 10.5 USD.

Other computation algorithms and formulae can be used instead of thedistance/proximity measures ones provided above. The important factorsare:

-   -   1) Application events are processed to analyze the application        usage.    -   2) Formula calculates measurable impact of the application,        using other valuable events that are significant (hence create        value).    -   3) Based on the impact calculation, applications can then be:        -   a. Compared to other application for value (either monetary            or relative);        -   b. Analyzed for financial return (return on investment, or            ROI);        -   c. Removed with negative/low impact;        -   d. Charged to the eco-system (e.g., advertisers) for using            the application based on the relative value;        -   e. Included in research groups of applications for multiple            inter-application impacts.

In addition, the invention event and impact calculation can be used incontinuous manner on all applications, perpetually, in order tostimulate creation of data that will be then analyzed for trends, longterm projections, etc. In this alternative embodiment of the invention,there is no specific configuration of the applications or events, and asa default, all events proximities are calculated, demanding moreresources for the computations, but allowing more data driven analysisas required.

While the present invention has been described with respect to what ispresently considered to be the preferred embodiment, it is to beunderstood that the invention is not limited to the disclosedembodiments. To the contrary, the invention is intended to cover variousmodifications and equivalent arrangements included within the spirit andscope of the appended claims. The scope of the following claims is to beaccorded the broadest interpretation so as to encompass all suchmodifications and equivalent structures and functions.

1. A system for assessing a value of a first application with respect toa use of a first mobile communication device, the system comprising atleast one computer server in communication with the first mobilecommunication device via a network and at least a second communicationdevice in communication with the first mobile communication device viathe network, wherein the server is configured to: detect a use of thefirst mobile communication device with respect to the secondcommunication device, wherein the detected use includes a use of thefirst application and a communication with the second communicationdevice; assign at least a first proximity value to the detected use ofthe first mobile communication device with respect to the secondcommunication device and the use of the first application; use theassigned at least first proximity value to determine an impact of thedetected use of the first application upon the detected use of the firstmobile communication device; and use the determined impact to assess avalue of the detected use of the first application with respect to thedetected use of the first mobile communication device.